diff --git a/.gitignore b/.gitignore
index ad7ed55f68e44de94fae3b12faaa7ca67a5ec9d0..018fe0242d2b50b18b5e8d085fe6882aa34cfa74 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,9 @@ Debug/*
*.root
*.fits
*.dat
+*.rmf
+*.arf
+*.npy
response_matrix*/*
results*/*
background*/*
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diff --git a/python/__pycache__/rmf.cpython-312.pyc b/python/__pycache__/rmf.cpython-312.pyc
index f4a1a4612c032a37d1f0db18f7ed0d4a64c4f231..101047b6449a65716327c81749d0c022a1d2694f 100644
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diff --git a/python/aeFF.py b/python/aeFF.py
index 226d6b3095b4b0d06242d0457906a8195024ee4a..ea646e2571ec935be46c7f1923e0e8a1b72c242a 100644
--- a/python/aeFF.py
+++ b/python/aeFF.py
@@ -8,19 +8,27 @@ from arf import arf_file
from rmf import rmf_file
def main():
- # 1 Estract data from fits file
+ # 1 Estract data from fits file and give these output file_dat
file_dat = "areaEfficace.dat"
data_estraction(file_dat)
#2 Filter data
- data = np.loadtxt(file_dat)
+ data = np.loadtxt(file_dat, dtype=str)
- energy = data[:, 0]
- ratio_X = data[:, 1]
- ratio_S = data[:, 2]
- theta = data[:, 3]
- phi = data[:, 4]
+ energy = data[:, 0].astype(float)
+ ratio_X = data[:, 1].astype(float)
+ ratio_S = data[:, 2].astype(float)
+ theta = data[:, 3].astype(float)
+ phi = data[:, 4].astype(float)
+ en_dep_file = data[:, 5]
+ #en_dep_arrays = [np.load(file) for file in en_dep_files]
+
+ # Stampa i dati di en_dep
+ #for en_dep_array in en_dep_arrays:
+ # print(en_dep_array)
+ #print(en_dep_file)
+
# Ottieni tutte le combinazioni uniche di theta e phi
angle_combinations = np.unique(np.column_stack((theta, phi)), axis=0)
@@ -39,6 +47,7 @@ def main():
energy_filtered = energy[mask]
ratio_X_filtered = ratio_X[mask]
ratio_S_filtered = ratio_S[mask]
+ en_dep_file_filtered = en_dep_file[mask]
# Salva i dati per il plot
plot_data_X.append((energy_filtered, ratio_X_filtered, theta_val, phi_val))
@@ -48,10 +57,10 @@ def main():
#arf_file(energy_filtered, ratio_X_filtered, ratio_S_filtered, theta_val, phi_val)
# 4 RMF
- print(energy_filtered)
- print(theta_val)
- print(phi_val)
- rmf_file(energy_filtered, theta_val, phi_val)
+ #print(energy_filtered)
+ #print(theta_val)
+ #print(phi_val)
+ rmf_file(en_dep_file_filtered, theta_val, phi_val, energy_filtered)
# 5 Plot
diff --git a/python/convertRoot2Fits.py b/python/convertRoot2Fits.py
index 273af6f50497eb60841f01143be6e08e344f898a..ac5a2ae8ac5d198ae187f7334056d7c36f1a1b6b 100644
--- a/python/convertRoot2Fits.py
+++ b/python/convertRoot2Fits.py
@@ -94,7 +94,7 @@ def generate_fits(energy, theta_deg, phi_deg):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
- output_file = os.path.join(output_dir, "AA" + input_file.split(".")[0] + f"_{energy}" + f"_{theta_deg}" + f"_{phi_deg}" + ".fits")
+ output_file = os.path.join(output_dir, input_file.split(".")[0] + f"_{energy}" + f"_{theta_deg}" + f"_{phi_deg}" + ".fits")
hdulist.writeto(output_file, overwrite=True)
# os.system("gzip -f " + input_file.split(".")[0] + ".fits")
diff --git a/python/data.py b/python/data.py
index 10b7f1f73c8a1b6f69b31750d0d894f0db3b0254..ea391c570516de05de736bcc64b2654332b011ea 100644
--- a/python/data.py
+++ b/python/data.py
@@ -1,4 +1,5 @@
import os
+import numpy as np
from multiprocessing import Pool
import astropy.io.fits as fits
@@ -19,17 +20,27 @@ def get_data(files):
tot_events = len(data)
scint_id_col = data['Scint_ID']
energy = data['En_Primary'][0]
+ en_dep = data['En_dep']
eventi_X = (scint_id_col == -1000).sum()
eventi_S = tot_events - eventi_X
ratio_X = eventi_X/tot_events
ratio_S = eventi_S/tot_events
- return energy, ratio_X, ratio_S, theta, phi
+ return energy, ratio_X, ratio_S, theta, phi, en_dep
def write_results(file_dat, results):
+ npy_dir = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/npy"
+ if not os.path.exists(npy_dir):
+ os.makedirs(npy_dir)
+
with open(file_dat, "w") as f:
- for energy, ratio_X, ratio_S, theta, phi in results:
- f.write(f"{energy:.1f}\t{ratio_X:.3f}\t{ratio_S:.3f}\t{theta}\t{phi}\n")
+ for energy, ratio_X, ratio_S, theta, phi, en_dep in results:
+ # Salva l'array en_dep in un file separato, e salva tutto in una cartella
+ npy_file = f"en_dep_{energy:.1f}_{theta}_{phi}.npy"
+ en_dep_file = os.path.join(npy_dir, npy_file)
+ np.save(en_dep_file, en_dep)
+
+ f.write(f"{energy:.1f}\t{ratio_X:.3f}\t{ratio_S:.3f}\t{theta}\t{phi}\t{npy_file}\n")
# Estrae i dati, processa in parallelo tutti i file .fits presenti nella folder, usa le due funzioni già definite
def data_estraction(file_dat):
diff --git a/python/forRMF.py b/python/forRMF.py
deleted file mode 100644
index 34587ca9b653b6f20ce5109e43c62e0690232965..0000000000000000000000000000000000000000
--- a/python/forRMF.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import numpy as np
-import sys
-import matplotlib.pyplot as plt
-from astropy.io import fits
-import os
-
-bins = np.arange(0, 151, 5)
-
-def get_data(energy, theta, phi):
- os.chdir('/home/alfonso/Scrivania/THESEUS/xgis_m7-main/fits')
- # Percorso del file FITS
- file = f'scorefile_{energy}_{theta}_{phi}.fits' # se il file non esiste da errore ma va avanti con gli altri
-
- # Legge il file FITS
- with fits.open(file) as hdul:
- print(file)
-
- data = hdul[1].data # Legge i dati dalla seconda estensione
-
- # Estrae la colonna 'En_dep'
- en_dep = data['En_dep']
-
-
- # ! i bin dell isto possono essere gli stessi degli intervalli di energ della simulazione?
-
- # Imposta i bin dell'istogramma tra 0 keV e 150 keV, con bin larghezza di 5 keV
- #bins = np.arange(0, 151, 5) #! per ora lo metto fuori altrimenti non lo importa
-
-
- # Crea l'istogramma normalizzato per il numero totale di righe
- hist, bin_edges = np.histogram(en_dep, bins=bins, density=False)
- print(bin_edges)
- hist_normalized = hist / len(en_dep)
-
- # Calcola i centri dei bin (centro dell'intervallo per ogni bin di energia)
- bin_centers = 0.5 * (bin_edges[1:] + bin_edges[:-1])
-
- # Definisce i "channels" come un array di numeri interi, uno per ogni bin (da 1 a 30)
- channels = np.arange(1, len(bin_centers) + 1)
-
- print("Vettore dei conteggi per ciascun canale (binned data):")
- print(hist)
-
- print("Vettore dei conteggi NORMALIZZATI per ciascun canale (binned data):")
- print(hist_normalized)
-
- # Se vuoi vedere il vettore sotto forma di matrice (per una rappresentazione ordinata)
- print("Matrice dei conteggi:")
- for i in range(len(channels)):
- print(f"Channel {channels[i]}: {hist[i]}")
-
-
- # Inizializza variabili per il conteggio dei gruppi
- n_group = 0
- f_chan = [] # Indici di inizio dei gruppi
- n_chan = [] # Lunghezza dei gruppi
-
- # Controlla i gruppi di valori consecutivi non nulli
- in_group = False # Stato per controllare se siamo all'interno di un gruppo, booleano
- group_start = None # Per memorizzare l'inizio del gruppo, intero, noen= senza valore
-
- # Il ciclo scorre ogni valore dell'array hist_normalized e controlla se è maggiore di zero (cioè non nullo).
- for i in range(len(hist_normalized)):
- if hist_normalized[i] > 0:
- if not in_group: # se non sono gia in un gruppo svolgi i seguenti # ! questi comandi li esegue solo per i valori iniziali di ogni gruppo
- in_group = True # apro un nuovo gruppo
- group_start = i # Memorizza l'indice di inizio del gruppo
- f_chan.append(i) # Aggiungi l'indice di inizio al vettore f_chan
- n_group += 1 # Incrementa il numero di gruppi
- else:
- if in_group: # Termina il gruppo corrente # ! qui esegue solo al primo zero che trova, se ne trova uno consecutivo non esegue perche trova in_group falso
- in_group = False
- group_length = i - group_start # Calcola la lunghezza del gruppo
- n_chan.append(group_length) # Aggiungi la lunghezza del gruppo a n_chan
-
- # Controlla se l'ultimo gruppo arriva fino alla fine dell'array
- if in_group: # se è true esegue
- n_chan.append(len(hist_normalized) - group_start)
-
- f_chan = np.array(f_chan)
- n_chan = np.array(n_chan)
-
- # Stampa i risultati
- print(f"Numero di gruppi trovati: {n_group}")
- print(f"Indici di inizio dei gruppi (f_chan): {f_chan}")
- print(f"Lunghezza di ciascun gruppo (n_chan): {n_chan}")
-
-
-
- # # Crea il grafico con i channel sull'asse x e i conteggi normalizzati sull'asse y
- # plt.figure(figsize=(10, 6))
- # plt.bar(channels, hist_normalized, width=1, edgecolor="black", align="center")
-
- # # Etichetta gli assi
- # plt.xlabel('Channel')
- # plt.ylabel('Normalized Count')
- # plt.title('Normalized Histogram with Channels')
-
- # # Modifica l'asse x per mostrare i channel
- # plt.xticks(channels)
-
- # Se vuoi vedere anche la relazione tra bin di energia e channel, puoi stampare:
- for i in range(len(channels)):
- print(f'Channel {channels[i]} corrisponde al bin di energia centrato a {bin_centers[i]:.2f} keV')
-
-
- # Salva il grafico
- directory = '/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/figure'
- if not os.path.exists(directory):
- os.makedirs(directory)
-
- # # cosi non salva
- # file_name = "HISTONE.png"
- # file_path = os.path.join(directory, file_name)
- # plt.savefig(file_path, bbox_inches='tight')
-
- # # Mostra il grafico
- # plt.show()
- # plt.close()
-
- return n_group, f_chan, n_chan, hist_normalized, channels
\ No newline at end of file
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diff --git a/python/rmf.py b/python/rmf.py
index 17639f00de942bd7d170c31ebee92ae98734a19e..a9c7aa68da2976e6b7d035e9c111b19456f3d05e 100644
--- a/python/rmf.py
+++ b/python/rmf.py
@@ -2,7 +2,6 @@ import astropy.io.fits as pyfits
import os
import sys
import numpy as np
-from forRMF import get_data, bins
def create_ebounds_header(extension):
@@ -35,7 +34,7 @@ def create_matrix_header(extension):
extension.header['HDUCLAS1'] = 'RESPONSE'
extension.header['HDUCLAS2'] = 'RSP_MATRIX'
extension.header['HDUVERS'] = '1.3.0'
- extension.header['TLMIN#'] = 'NONE' # the first channel in the response. # is the column number for the F_CHAN column (see below).
+ #extension.header['TLMIN#'] = 'NONE'# the first channel in the response. # is the column number for the F_CHAN column (see below).
# optional
extension.header['NUMGRP'] = 'NONE' # the total number of channel subsets. The sum of the N_GRP column.
extension.header['NUMELT'] = 'NONE' # the total number of response elements. The sum of the N_CHAN column
@@ -49,73 +48,108 @@ def create_matrix_header(extension):
# altre mandatory che non ho capito
+def estract_row(en_dep_file, bins):
+
+ en_dep = np.load(en_dep_file)
+ lenght = en_dep.size
+
+ counts, bin_edges = np.histogram(en_dep, bins=bins)
+ counts_norm = counts/lenght
+
+ print(counts)
+ print(counts_norm)
+
+ n_grp = 0
+ f_chan = []
+ n_chan = []
+
+ # Controlla i gruppi di valori consecutivi non nulli
+ in_group = False # Stato per controllare se siamo all'interno di un gruppo, booleano
+ group_start = None # Per memorizzare l'inizio del gruppo, intero, noen= senza valore
+
+ # Il ciclo scorre ogni valore dell'array counts_norm e controlla se è maggiore di zero (cioè non nullo).
+ for i in range(len(counts_norm)):
+ if counts_norm[i] > 0:
+ if not in_group: # se non sono gia in un gruppo svolgi i seguenti # ! questi comandi li esegue solo per i valori iniziali di ogni gruppo
+ in_group = True # apro un nuovo gruppo
+ group_start = i # Memorizza l'indice di inizio del gruppo
+ f_chan.append(i) # Aggiungi l'indice di inizio al vettore f_chan
+ n_grp += 1 # Incrementa il numero di gruppi
+ else:
+ if in_group: # Termina il gruppo corrente # ! qui esegue solo al primo zero che trova, se ne trova uno consecutivo non esegue perche trova in_group falso
+ in_group = False
+ group_length = i - group_start # Calcola la lunghezza del gruppo
+ n_chan.append(group_length) # Aggiungi la lunghezza del gruppo a n_chan
+
+ # Controlla se l'ultimo gruppo arriva fino alla fine dell'array
+ if in_group: # se è true esegue
+ n_chan.append(len(counts_norm) - group_start)
+
+ return n_grp, f_chan, n_chan, counts_norm # row_matrix = counts_norm
-# PER OGNI ENERGIA DEVE CHIAMARE FORRMF E FARSI PASSARE I DATI
-
-def rmf_file(energy_filt, theta, phi):
+def rmf_file(en_dep_file_filt, theta, phi, energy):
try:
+ os.chdir("/home/alfonso/Scrivania/THESEUS/xgis_m7-main/python/npy")
+
+ n_grp_col = []
+ f_chan_col = []
+ n_chan_col = []
+ row_matrix_col = []
+
+ bins = np.arange(0, 156, 5) # se tra min e max voglio definire una spaziatura # ! per ora questo ma meglio linspace
+ #bins = np.linspace(0, 156, 5) # se tra min e max voglio definire quanti bin voglio
+
+ # se voglio definire i channels
+ bin_centers = 0.5 * (bins[1:] + bins[:-1])
+ channels = np.arange(1, len(bin_centers) + 1)
+
+ for en_dep_file in en_dep_file_filt:
+ n_grp, f_chan, n_chan, row_matrix = estract_row(en_dep_file, bins)
+
+ n_grp_col.append(n_grp)
+ f_chan_col.append(f_chan)
+ n_chan_col.append(n_chan)
+ row_matrix_col.append(row_matrix)
+
+ print(f"n_grp: {n_grp_col}")
+ print(f"f_chan: {f_chan_col}")
+ print(f"n_chan: {n_chan_col}")
+ print(f"row_matrix: {row_matrix_col}")
+
+
# Creazione di "Null" primary array
primary_hdu = pyfits.PrimaryHDU()
+
-
- # chan = [1,30]
- #lista = np.arange(1, 31)
- # relaivo all'energia di output
# Creazione di ebounds BinTableHDU per EVENTI X
- #ebounds_bin_tableX_hdu = pyfits.BinTableHDU.from_columns([
- # pyfits.Column(name='CHANNEL', format='1J', array=lista),
- # pyfits.Column(name='E_MIN', format='1E', unit='keV', array=bins[:-1]),
- # pyfits.Column(name='E_MAX', format='1E', unit='keV', array=bins[1:]),
- # ])
- #create_ebounds_header(ebounds_bin_tableX_hdu)
- print(energy_filt)
- for energy in energy_filt:
- print(energy)
- n_grp, f_chan, n_chan, hist_normalized, channels = get_data(energy, theta, phi)
-
- print(bins)
- print(f"n_grp: {n_grp}")
- print(f"f_chan: {f_chan}")
- print(f"n_chan: {n_chan}")
- print(channels)
- print(f"hist_normalized: {hist_normalized}")
- # relativo all energia di input
- # Creazione di matrice BinTableHDU per EVENTI X
- matrix_bin_tableX_hdu = pyfits.BinTableHDU.from_columns([
- pyfits.Column(name='ENERG_LO', format='1E', unit='keV', array = bins[:-1]),
- pyfits.Column(name='ENERG_HI', format='1E', unit='keV', array = bins[1:]),
- pyfits.Column(name='N_GRP', format='1I', array = [n_grp]),
- pyfits.Column(name='F_CHAN', format='3J', array = f_chan),
- pyfits.Column(name='N_CHAN', format='3J', array = n_chan),
- pyfits.Column(name='MATRIX', format='33E', array = hist_normalized),
+ ebounds_bin_tableX_hdu = pyfits.BinTableHDU.from_columns([
+ pyfits.Column(name='CHANNEL', format='1I', array = channels),
+ pyfits.Column(name='E_MIN', format='1E', unit='keV', array = bins[:-1]),
+ pyfits.Column(name='E_MAX', format='1E', unit='keV', array = bins[1:]),
])
+ create_ebounds_header(ebounds_bin_tableX_hdu)
+ # Creazione di matrice BinTableHDU per EVENTI X
+ matrix_bin_tableX_hdu = pyfits.BinTableHDU.from_columns([
+ pyfits.Column(name='ENERG_LO', format='1E', unit='keV', array = energy[:-1]),
+ pyfits.Column(name='ENERG_HI', format='1E', unit='keV', array = energy[1:]),
+ pyfits.Column(name='N_GRP', format='1I', array = n_grp_col),
+ pyfits.Column(name='F_CHAN', format='PJ', array = f_chan_col),
+ pyfits.Column(name='N_CHAN', format='PJ', array = n_chan_col),
+ pyfits.Column(name='MATRIX', format='PE', array = row_matrix_col),
+ ])
+
create_matrix_header(matrix_bin_tableX_hdu) # Aggiunta header comune per gli eventi X
-
-
-
-
-
-
-
-
-
- # !!!! da fare anche per eventi S !!!!!!!!!!!!!!
-
-
-
-
-
-
- #ebounds_bin_tableX_hdu,
+
+ # !!!! da fare anche per eventi S !!!!!!!!!!!!!!
# Creazione di HDUList e scrittura del file FITS
- hdulX = pyfits.HDUList([primary_hdu, matrix_bin_tableX_hdu])
+ hdulX = pyfits.HDUList([primary_hdu, ebounds_bin_tableX_hdu, matrix_bin_tableX_hdu])
#hdulS = pyfits.HDUList([primary_hdu, ebounds_bin_tableS_hdu, matrix_bin_tableS_hdu])
output_dir = "/home/alfonso/Scrivania/THESEUS/xgis_m7-main/rmf"
@@ -131,9 +165,4 @@ def rmf_file(energy_filt, theta, phi):
print("File .rmf creati correttamente.")
except Exception as e:
- print(f"Errore durante la creazione del file .rmf: {e}")
-
-
-
-# questa chiamata di funzione appena funziona tutto la porta in aeFF.py
-#rmf_file()
+ print(f"Errore durante la creazione del file .rmf: {e}")
\ No newline at end of file
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